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Context-Aware Recommendation Systems has gained lots of attention in both industry and academic research. Factorization Machines (FM) based recommendation has been successfully used in sparse industrial datasets for user personalized video recommendations. FM is a collaborative filtering technique for predicting a target such as user rating, given observations of interaction between some users and...
DNA Microarray data is a high-dimensional data that enables the researchers to analyze the expression of many genes in a single reaction quickly and in an efficient manner. Its characteristics such as small sample size, class imbalance, and data complexity causes it difficult to classified. Feature selection is a process that automatically selects features that are most relevant to the predictive...
In the world, many currencies have been issued and some of them were counterfeited. Especially, by the advances in copying technology and computer ability it is rather easy to make counterfeiting bills. In this paper, we develop a method to use spectral property of the bills and classify the bill whether it is true or fake using spectral sub-band analysis for Singapore dollars.
In this study, artificial learning approach which can express high dimensional data in a lower space (autocoding) and known as “autoencoder” in the literature has been investigated in detail without using a predefined ready mathematical model. The most important feature of this method, which can be used in place of traditional feature extraction methods (HOG, SHIFT, SURF, Wavelet, etc.), is the ability...
Estimating short-term power load is a fundamental issue in the power distribution system. Since short-term power load is related to many parameters such as weather conditions, and time. The aim of this study is to determine the relevant parameters in estimating short-term power load not only in order to decrease the computational cost, but also to achieve higher success rates. Furthermore, by using...
Dealing with high dimensional data is a challenging and computationally complex task in the data pre-processing phase of text clustering. Conventionally, union and intersection approaches have been used to combine results of different feature selection methods to optimize relevant feature space for document collection. Union method selects all features from considered sub-models, whereas, intersection...
Herlev dataset consists of 7 cervical cell classes, i.e. superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ is considered. The dataset will be tested to classify two classes, consisting of normal and abnormal cells. Seven different cell types will be classified to separate the cells into 7 classes which are 3 normal cell...
The medical datasets have many features if the features have a tendency of mutation then the risk of disease increases which makes difficult to provide a diagnosis of disease. In the dataset, every feature is a contributor for prediction accuracy, the selection of significant features from the dataset is a challenging task. The feature selection technique based on metaheuristic algorithms is used...
In this paper we present a solution for the classification of different patterns from seismic signals generated by different human activities for which an automatic recognition is required. Some a priori known signals were available, which gave us the possibility to represent them in feature space in order to capture their global characteristics. The classification of signals is based on computing...
Face recognition became a daily discipline in human life. At the work, with our PDP and smart phones, for our daily help, our security and many other utilities, face recognition has crossed the laboratory doors and colonized the human quotidian. However, the effectiveness of the developed applications still encounters many challenges. The presented work in this paper tries to deal with these challenges...
Border Gateway Protocol (BGP) anomalies affect network operations and, hence, their detection is of interest to researchers and practitioners. Various machine learning techniques have been applied for detection of such anomalies. In this paper, we first employ the minimum Redundancy Maximum Relevance (mRMR) feature selection algorithms to extract the most relevant features used for classifying BGP...
With the rapid growth of Internet consumption, the various product comments' form and redundant information are not convenient for the customers to grasp the hot opinions of the historical comments. In view of this, this paper studies the hot opinions of the products' comments and takes the hotel comments data as the main research objects. We filter the comment data from the length of the comments...
In this paper, we propose a hybrid classification model, which has correlation based filter feature selection algorithm and support vector machine as a classifier. In this method, features are ordered according to their Absolute correlation value with respect to the class attribute. Then top K Features are selected from ordered list of features to form a reduced dataset. The classification accuracy...
In hyperspectral image analysis, the classification task has generally been discussed with dimensionality reduction due to high correlation and noise between the spectral features, which might cause significantly low classification performance. In supervised classification, limited training samples in proportion to the number of spectral features have also negative impacts on the classification accuracy,...
Misalignment is one of the most common mechanical faults in electrical rotating machinery, it can lead to partial or total breakdown of a motor in the long run may. This paper investigates the application of the Autoregressive Model of torque signal to detect and diagnose the misalignment fault. First, the torque signal obtained from experiment in different conditions: healthy condition, angular misalignment,...
According to the connections between the feature subset and RBF neural network parameter, in order to improve the accuracy rate of intrusion detection, a network intrusion detection model ( IPSO-BPNN ) improving the article swarm and optimizing the neural network is put forward. Take the network feature subset and RBF neural network parameter as a particle, and discover the optimum network feature...
Real-time, accurate and robust target tracking on mobile devices is an important problem which can facilitate applications such as augmented reality. However, it is still unsolved, partly due to the mobile's computing limitations. Compressive tracker performs favorably against state-of-the-art algorithms in terms of efficiency, accuracy and robustness, but as limited by the speed of feature matching,...
In modern times, it has become very essential for e-commerce businesses to empower their end customers to write reviews about the services that they have utilized. Such reviews provide vital sources of information on these products or services. This information is utilized by the future potential customers before deciding on purchase of new products or services. These opinions or reviews are also...
Visual surveillance applications play essential roles as a tool for archiving and to some extend preventing criminal activities. The future of visual surveillance applications relies on the developments in the area of computer vision, human motion analysis and computer algorithms. This paper considers objects low level feature selection and extraction from video frames in order to construct a feature...
This paper describes a pedestrian detection method using feature selection based on logistic regression analysis. As the parent features, Haar-like and Histograms of Oriented Gradients (HOG) features are used manually. For the statistical analysis, stepwise forward selection, backward elimination, and Least Absolute Shrinkage and Selection Operator (LASSO) methods are applied to our Logistic Regression...
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